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A new approach to extract meaningful clinical information from medical notes

机译:从医疗票据提取有意义的临床信息的新方法

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In medical domain, one of the most important documents is the notes that doctors, nurses or other medical practitioners take during patient interview. These notes contain important information about the patient current condition, symptoms, family history, disease, procedures (like x-ray, lab test etc.), medication and so on. These notes are in plain text English language and are presented in an unstructured way. An unstructured text can be defined as text which contains information not in a common structured format. Since these notes contain very useful clinical information, everyone is willing to extract such information. But lack of defined structure, it makes these texts to be interpreted only by humans and not by any computer program. In structured information, we have only the useful information extracted from the text and other unnecessary information is thrown away. This makes the information to be presented in a definite structure which can be stored in database for further processing. In this approach, we are going to build a medical corpus using the training dataset and apply the corpus in order to extract the information like diagnosis, procedure, drug, habit and vitals from the clinical notes and evaluate it based on different accuracy parameters.
机译:在医疗领域,最重要的文件之一是医生,护士或其他医生在患者面试中采取的指出。这些笔记包含有关患者当前条件,症状,家族史,疾病,程序(如X射线,实验室测试等),药物等的重要信息。这些笔记是纯文本英语语言,并以非结构化方式呈现。非结构化文本可以定义为包含不具有通用结构化格式的信息的文本。由于这些笔记包含非常有用的临床信息,因此每个人都愿意提取此类信息。但缺乏定义结构,它使这些文本仅由人类解释,而不是由任何计算机程序解释。在结构化信息中,我们只有从文本中提取的有用信息和其他不必要的信息被抛出。这使得能够以明确的结构呈现,该结构可以存储在数据库中以进行进一步处理。在这种方法中,我们将使用培训数据集建立医疗语料库,并应用语料库,以便从临床笔记中提取诊断,程序,药物,习惯和生命权的信息,并根据不同的准确性参数评估它。

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